intrusion detection

This is a part of the Cityintrusion-Multicategory dataset for testing and training the network. This dataset contains 2502 training images and 429 validation images. Because our task is a joint task of segmentation and detection. Therefore, we provide the two different sub-dataset for segmentation and detection, respectively. In the seg folder, we provide the original images for training and validation. Besides, the corresponding labels also are provided. Training and validation have 2502 and 429, respectively.

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This is a part of the Cityintrusion-Multicategory dataset for testing and training the network. This dataset contains 2502 training images and 429 validation images. Because our task is a joint task of segmentation and detection. Therefore, we provide the two different sub-dataset for segmentation and detection, respectively. In the seg folder, we provide the original images for training and validation. Besides, the corresponding labels also are provided. Training and validation have 2502 and 429, respectively.

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5 Views

ABSTRACT As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard including signature-based and machine-learning (ML) based systems. More recently, researchers are considering deep learning (DL) based anomaly detection approaches. Most proposed works in this research field aimed to achieve either one or a combination of high accuracy, considerably low false alarm rates (FARs), high classification specificity and detection sensitivity, achieving lightweight DL models, or other ML and DL-related performance measurement metrics. In this study, we propose a novel method to convert a raw dataset to an image dataset to magnify patterns. Based on this we devise an anomaly detection for IDS using a lightweight convolutional neural network (CNN) that classifies denial of service and distributed denial of service. The proposed methods were evaluated using a modern dataset, CSE-CIC-IDS2018, and a legacy dataset, NSL-KDD. We have also applied a combined dataset to assess the generalization of the proposed model across various datasets. Our experimental results have demonstrated that the proposed methods achieved high accuracy and considerably low FARs with high specificity and sensitivity. The resulting loss and accuracy curves have also demonstrated the excellent generalization of the proposed lightweight CNN model, effectively avoiding overfitting. This holds for both the modern and legacy datasets, including their mixed version.

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406 Views

The advancements in the field of telecommunications have resulted in an increasing demand for robust, high-speed, and secure connections between User Equipment (UE) instances and the Data Network (DN). The implementation of the newly defined 3rd Generation Partnership Project 3GPP (3GPP) network architecture in the 5G Core (5GC) represents a significant leap towards fulfilling these demands. This architecture promises faster connectivity, low latency, higher data transfer rates, and improved network reliability.

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2080 Views

With a plethora of new connections, features, and services introduced, the 5th generation (5G) wireless technology reflects the development of mobile communication networks and is here to stay for the next decade. The multitude of services and technologies that 5G incorporates have made modern communication networks very complex and sophisticated in nature. This complexity along with the incorporation of Machine Learning (ML) and Artificial Intelligence (AI) provides the opportunity for the attackers to launch intelligent attacks against the network and network devices.

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4813 Views

The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.

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6798 Views

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established.

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9523 Views

The Internet of Things (IoT) is reshaping our connected world, due to the prevalence of lightweight devices connected to the Internet and their communication technologies. Therefore, research towards intrusion detection in the IoT domain has a lot of significance. Network intrusion datasets are fundamental for this research, as many attack detection strategies have to be trained and evaluated using these datasets.

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1959 Views

This is the dataset provided and collected while "Car Hacking: Attack & Defense Challenge" in 2020. We are the main organizer of the competition along with Culture Makers and Korea Internet & Security Agency. We are very proud of releasing these valuable datasets for all security researchers for free.

The competition aimed to develop attack and detection techniques of Controller Area Network (CAN), a widely used standard of in-vehicle network. The target vehicle of competition was Hyundai Avante CN7.

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8503 Views

This dataset is from apache access log server. It contains: ip address, datetime, gmt, request, status, size, user agent, country, label. The dataset show malicious activity in IP address, request, and so on. You can analyze more as intrusion detection parameter.

Paper: http://jtiik.ub.ac.id/index.php/jtiik/article/view/4107

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3410 Views

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